Implemented restartAdjoint option

[sseraj]

Purpose

I implemented the restartAdjoint option, which previously did nothing. The default behavior, which is to use the previous adjoint solution as the initial guess, is unchanged.

Using "restartAdjoint": False improved my optimization convergence slightly. I don't know if this will be helpful in general, but it's a nice option to have even if it is just for debugging.

Type of change

• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (non-backwards-compatible fix or feature)
• Code style update (formatting, renaming)
• Refactoring (no functional changes, no API changes)
• Documentation update
• Maintenance update
• Other (please describe)

Testing

Checklist

• I have run flake8 and black to make sure the code adheres to PEP-8 and is consistently formatted
• I have run unit and regression tests which pass locally with my changes
• I have added new tests that prove my fix is effective or that my feature works
• I have added necessary documentation

[codecov]

Codecov Report

Merging #188 (dfca74e) into master (50ce904) will increase coverage by 0.00%.
The diff coverage is 66.66%.

@@           Coverage Diff           @@
##           master     #188   +/-   ##
=======================================
  Coverage   43.10%   43.10%           
=======================================
  Files          15       15           
  Lines        3517     3519    +2     
=======================================
+ Hits         1516     1517    +1     
- Misses       2001     2002    +1     

Impacted Files	Coverage Δ
adflow/pyADflow.py	67.44% <66.66%> (-0.02%)	⬇️

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[marcomangano]

Nice! Did you notice significant increases in computational wall time?

[sseraj]

Nice! Did you notice significant increases in computational wall time?

For my case, there was no increase in wall time for the adjoint solution. This is because the adjoint convergence stalls before reaching the prescribed tolerance (due to #4), so the solver always runs for the maximum number of iterations. For a regular case where the adjoint converges normally, I would expect a slight increase in wall time.